Method for isolation and restoration for a multi core sensor system within a taxi

ABSTRACT

A method for isolation and restoration for a multi-core sensor system within a taxi is provided. This method can intelligently determine whether the reason for an abrupt dramatic change in the data detected by sub-sensor is a sensor fault or sudden pollution, so as to increase the reliability of the data detected by the sub-sensor. This method can automatically determine if the repair can be performed when a device fault occurs, so as to ensure the continuity of the detection data of the sub-sensor, which has significant value for continuous monitoring required for a haze treatment operation. In addition, human and material resources for device maintenance may be saved, thereby reducing waste.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2019/074037, filed on Jan. 31, 2019, which claims the benefit ofpriority from International Application No. PCT/IB2018/055531, filed onJul. 25, 2018, and Chinese Patent Application No. 201810102149.9, filedon Feb. 1, 2018. The content of the aforementioned applications,including any intervening amendments thereto, is incorporated herein byreference.

TECHNICAL FIELD

This application relates to environmental monitoring, and particularlyto a method for isolation and restoration for a multi-core sensor systemwithin a taxi.

BACKGROUND OF THE DISCLOSURE

The monitoring indicators of atmospheric pollutants in environmentalmonitoring include sulfur dioxide, nitrogen oxides, ozone, carbonmonoxide, PM₁ (particles with aerodynamic particle size less than 1 μm),PM_(2.5) (particles with aerodynamic particle size less than 2.5 μm),PM₁₀ (particles with aerodynamic particle size less than 10 μm), PM₁₀₀(particles with aerodynamic particle size less than 100 μm), and VOCs(volatile organic compounds) or TVOC (total volatile organic compounds).The atmospheric environment monitoring system can collect and processthe monitoring data, and reflect the air quality condition and changelaw of the regional environment in a timely and accurate manner.

At present, the atmospheric environment monitoring equipment mainlyincludes fixed monitoring stations and mobile monitoring equipment. Thecurrent fixed monitoring stations are mainly divided into large fixedmonitoring stations and small monitoring stations. Mobile monitoringequipment mainly includes special atmospheric environmental monitoringvehicles, drones and handheld devices. The aforementioned smallmonitoring stations and handheld devices all use air quality sensors tomeasure pollutants in the atmosphere. Air quality sensors have thecharacteristics of low cost, miniaturization and online monitoring, andcan be used on a large scale. However, the air quality sensor itself haserrors due to various reasons that cause the measured value to beinconsistent with the true value, and it also has low accuracy, poorstability, large errors, and requires frequent calibration compared withlarge precision instruments or manual monitoring methods.

The laser scattering method for air pollution particulate matter sensorshas a broad market prospect because of its low cost and portability. Inthe prior art, the portable analysis device of the air particulatematter sensor using the laser scattering method has disadvantages suchas poor measurement consistency, large noise, and low measurementaccuracy. The core device is susceptible to various environmentalfactors, and the fluctuations of the core device easily causemisjudgment.

When the sensor data changes suddenly and sharply, being able tointelligently determine whether the change is due to sensor failure orsudden pollution will greatly improve the reliability of the data and isof great value for ensuring the quality of monitoring data. When theequipment fails, if it can be repaired automatically, the online rate ofthe data can also be greatly improved, which is of great value for thecontinuous monitoring required for haze control. At the same time, itcan save manpower and material resources in equipment maintenance andreduce social waste.

Chinese Patent Application Publication No. 105823856 A discloses an airquality monitoring method based on multi-sensor fusion, which fusesmultiple sets of measurement data from multiple sensors to optimize theproblem of pseudo-random errors introduced by the fluctuation of light.The data fusion method can select the existing fusion algorithmaccording to the requirements.

The article discloses that when the scattering method is used to measurepollutants in the air, the emitted laser is in the range of severalhundred nanometers to more than one thousand nanometers, And forPM_(2.5) (particle diameter below 2500 nm) and PM₁₀ (particle diameterbelow 10000 nm) pollutants to be tested, the visible laser wavelength isequivalent to the particle size of the pollutant to be measured. Thelaser light shows both fluctuation and particle at the same wavelength,and the scattering effect used by the light scattering method can onlybe measured by the particle of light, so the one time measurement cannotfully accurately represent the number of particles in the space to bemeasured.

Chinese Patent Application Publication No. 101763053 A discloses adetection system with a real-time self-diagnostic function, capable ofidentifying sensor failure, signal abnormality, subsystem functionfailure, or system abnormality. When the sensor fails, the system canimmediately upload the fault information and activate the alarm; at thesame time, isolate the fault sensor.

Chinese Patent Application Publication No. 102480783 A discloses awireless sensor system, which can make redundant nodes in the networkrotational rest irregularly through the watch keeping schedulingmechanism to extend the life.

SUMMARY OF THE DISCLOSURE Terminology

Sensor: A sensor is a detection device that can sense the concentrationinformation of pollutants and can convert the sensed information intoelectrical signals or other required forms of information output inaccordance with a certain rule to meet the transmission, processing,storage, display, record, and control requirements. The pollutants inthis article mainly include particulate matter (PM₁, PM_(2.5), PM₁₀,PM₁₀₀), nitrogen oxides, sulfur dioxide, ozone, VOCs/TVOC and carbonmonoxide.

Sub-sensor: It is also called sub-sensor unit. In this article, thesub-sensor includes fan, sensing element, MCU, signal conversion elementand signal amplification circuit. It can independently complete thecollection and calculation of pollutant data and can also transmit tolocal for data storage.

Sensor module: The sensor module is a sensor device composed of multiplesub-sensors. The sub-sensors are also called cores in the sensor module.For example, a sensor module consisting of four sub-sensors is called aquad-core sensor, and a sensor module consisting of five sub-sensors isalso called a five-core sensor.

Abnormal fluctuation of sub-sensor: indicates that the discrete degreeof the measurement results of the sub-sensor during continuousmeasurement exceeds the normal range.

Abnormal drift of sub-sensor: It means that the average value of themeasurement result of the sub-sensor during continuous measurement isshifted from the true value beyond the normal range.

Abnormal correlation of sub-sensor: indicates that the correlationbetween the measurement results of the sub-sensor during the continuousmeasurement and other sub-sensors is lower than the normal range.

Abnormal of sub-sensor: Abnormal fluctuation of sub-sensor, abnormaldrift of sub-sensor, and abnormal correlation of sub-sensor are allabnormal of sub-sensor.

Abnormal sub-sensor: Also called a fault sub-sensor, it is a sub-sensorin which the abnormal phenomenon of the sub-sensor occurs.

The suspected abnormal sub-sensor: Also called suspected faultysub-sensor, in the sensor module, the sub-sensor with the largestfluctuation or drift does not trigger the isolation condition; that is,the degree of fluctuation or offset cannot make it be regarded as anabnormality of the sub-sensor. The suspected abnormal sub-sensor is theclosest abnormal of sub-sensor among the normal sub-sensors. Forexample, if the measured value deviates from the normal value by 20%, itis judged to be abnormal. Assuming that the number 1, 2 and 3sub-sensors deviate from the normal values by 5% and 6%, 16%, and thenwe judge the sub-sensor 3 is a suspected abnormal sub-sensor.

Isolation: The case where the sub-sensor does not participate in theoperation of the value uploaded by the control module is calledsub-sensor isolation.

Isolation condition: The isolation condition is used to determinewhether the suspected abnormal sub-sensor needs to be isolated. Such asthe value of the degree of dispersion in the abnormal fluctuation ofsub-sensor, and the offset value of the abnormal drift of sub-sensor.

Recovery condition: The recovery condition is the basis for judgingwhether the sub-sensors in the isolation zone will resume work. Thestandard of the recovery condition should be appropriately higher thanthe isolation condition, and there should be a difference of at least10% from each other to avoid the newly recovered sub-sensors from beingisolated again.

Rotational rest method: It is a kind of working method of sub-sensors,which means that the sub-sensors start and stop work alternately atintervals.

Data deterioration: indicates that the range of sub-sensor valuedeviates from the normal value increases.

Due to various reasons, for example, the performance of the sub-sensoritself and the influence of external interference, there is often asmall error between the measured value and the true value of the airquality sensor. Reducing errors and improving accuracy are the effortsin the field of sensors.

There are also many ways to improve sensor accuracy.

The first method is to use a single high-cost and high-precision sensor,but the problems brought by it are also obvious. In addition to the highcost problem, it is not possible to determine whether the sensor isabnormal through the data output by the sensor itself.

The second method is a dual-core sensor, which independently measuresand outputs the results through two sub-sensors. This method can comparethe output results of the two sub-sensors according to a certainjudgment standard to determine whether the sub-sensor works abnormally,but this method cannot determine which sub-sensor has an abnormality.

The third method is a triple-core sensor. By comparing the outputresults of the three sub-sensors, it is determined which sub-sensor hasa problem, and then isolates the sub-sensor. However, since the sensormodule runs in dual-core mode after isolating a sub-sensor, there willbe a problem that the abnormal sensor cannot be judged. Therefore, onceone sub-sensor of the three-core sensor is abnormal, the reliability ofthe whole sensor module is greatly reduced.

FIG. 1 shows the working state of the sub-sensor. The sub-sensor 100indicates a normal sub-sensor. The sub-sensor 101 and the sub-sensor 102are suspected abnormal sub-sensors. The sub-sensor 104 indicates anabnormal sub-sensor. In FIG. 2, 1U indicates a one-core sensor mode.When the sub-sensor data is abnormal, it cannot be determined whetherthe sensor itself is faulty or the air quality is abnormal. 2U indicatesa dual-core sensor module. When the dual-core sensor module has asub-sensor output abnormal, it cannot determine which one is abnormal,so one sub-sensor of the dual-core sensor module is abnormal, and theentire module cannot work normally. By analogy, 3U represents athree-core sensor module.

In view of the above-mentioned shortcomings, the present disclosureprovides a method for isolation and restoration for a multi-core sensorsystem within a taxi. The disclosure uses a sensor module consisting ofat least four sub-sensor, which realizes complementary data deviationsand mutual verification, and improves the reliability, consistency,accuracy and life of the sensor module.

As shown in FIGS. 3 and 4, 4U represents a quad-core sensor module. Whena sub-sensor is detected to have a suspected abnormality, and thesuspected abnormal sub-sensor further shows sub-sensor abnormality, thesub-sensor is determined as an abnormal sensor and isolated. Thequad-core sensor module downgraded to a three-core sensor module, thethree-core sensor module can still work normally. 5U means a five-coresensor module. When a sub-sensor is detected to have a suspectedabnormality, and the suspected abnormal sub-sensor further showssub-sensor abnormality, the sub-sensor is determined as an abnormalsensor and isolated. The five-core sensor module is downgraded to aquad-core sensor module, and the quad-core sensor module can still worknormally; and so on, the six-core sensor module, the seven-core sensormodule and more core sensor modules.

The multi-core sensor system is installed in the ceiling light of thetaxi; the multi-core sensor system includes a gas separation box, acontrol module and a detection module. The gas separation box is used todistribute the measured gas to each individual sub-sensor. The gas inletof the gas separation box is connected to the gas sampling head, and thegas outlet is connected to the air inlet of each sub-sensor of thedetection module. The detection module is a sensor module with four ormore sub-sensors built in and it is used to detect the concentration ofatmospheric pollutants. The control module is used to receive, analyzeand upload the data detected by the detection module, and supply powerto the detection module.

The sub-sensor types include PM₁ sensor, PM_(2.5) sensor, PM₁₀ sensor,PM₁₀₀ sensor, sulfur dioxide sensor, nitrogen oxide sensor, ozonesensor, carbon monoxide sensor, VOCs sensor, TVOC sensor and othersensors that can measure the concentration of environmental pollutants.

The detection accuracy of the sub-sensor is related to many factors,such as the measured gas flow rate and temperature. The disclosurefurther improves the detection accuracy of the sensor module bydesigning in various ways.

The detection accuracy of the sub-sensor is related to temperature. Asshown in FIG. 8, the sub-sensor has an optimal operating temperaturerange. When the temperature is higher than the optimal operatingtemperature, the detection accuracy will decrease. In the disclosure,the temperature of the sensor and the intake air are adjusted by atemperature control device, and can be compensated by an algorithm toimprove the detection accuracy.

The detection accuracy of the sub-sensor is also related to the flowrate of the measured gas flowing inside the sub-sensor. As shown in FIG.9, the measured gas has the highest accuracy at the optimal flow rateV₀. Too fast or too slow the measured gas flow rate will affect thedetection accuracy of the sub-sensor. The internal air resistance of thesub-sensor or other reasons will cause the measured gas flow rate tochange, as shown in FIG. 10. The present disclosure controls themeasured gas flow rate within the optimal flow rate range by adjustingthe internal fan speed or other flow rate adjustment methods to improvethe detection accuracy of the sub-sensor.

Multi-core sensor modules use embedded algorithms to solve the problemof out-of-synchronization of multiple sub-sensors in detecting samplinggas due to different lengths of intake pipes, thereby obtaining moreaccurate detection data.

Multi-core sensor modules use multiple sub-sensors to measure airquality at the same time, and the output value is the average value ofmultiple sub-sensors, with high data accuracy. FIG. 5 shows the outputdata of the quad-core sensor module, where U1, U2, U3, and U4 are theoutput data of the four sub-sensors, and the solid line Average is theaverage of the four sub-sensors, so the output data is smoother,Stability and higher accuracy.

Laser sensor performance is affected by light decay of laser. As thesemiconductor laser is used for a longer time, the problem of opticalpower attenuation due to semiconductor materials and productionprocesses will occur. When the optical power attenuation reaches acertain level, the accuracy of the laser sensor detection data will beaffected.

In order to understand the variation degree of light attenuation of agroup of laser sensors after working for a long time, the disclosuredivides the sensor group into a high frequency group and a low frequencygroup, in which the low frequency group serves as a redundant unit toprovide a calibration basis for the high frequency group.

The disclosure discloses another multi-core sensor system, which isinstalled in a ceiling lamp of a taxi; the multi-core sensor systemincludes a control module and a detection module. The detection modulecomprises a sensor module consisting of at least two similar sub-sensorsand the sub-sensor operates at a normal operating frequency. Thedetection module also includes a low-frequency calibration moduleconsisting of at least one sub-sensor, and the sub-sensor inlow-frequency calibration module is similar to the sub-sensor in thesensor module; the sub-sensor in the low-frequency calibration moduleoperates at a frequency lower than the operating frequency of the sensormodule. Therefore, the low-frequency calibration module is also called alow-frequency group. For comparison, the sensor module is also called ahigh-frequency group.

Generally, the operating frequency of the sensor module is 10 times ormore than that of the low-frequency calibration module. The sensormodule and the sub-sensors of the sensor module have the same operatingfrequency, and the low-frequency calibration module and the sub-sensorsof the low-frequency calibration module have the same operatingfrequency. The ratio of the operating frequency of the sensor module tothe operating frequency of the low-frequency calibration module iscalled the high frequency and low frequency ratio, and can be set as:2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 15:1, or 20:1.

The operating frequency of the low-frequency calibration module can beconsistent with the rhythm of abnormal judgment. That is, when it isnecessary to determine whether there is the phenomenon of an abnormalsub-sensor in the sensor module, the low-frequency calibration moduleperforms the detection work.

Because the laser power attenuation is slow in most of the working lifeof the laser sensor, the accuracy of its data can be restored bycalibration; that is, the sub-sensor that is not attenuated or has avery low attenuation is used to calibrate the high-attenuatedsub-sensor.

During the operation of the sensor module, every certain time, such as 1day, 1 week, or 1 month, use the detection data of the low-frequencycalibration module as a reference to calibrate the detection data of thesensor module, and the calibration coefficient can be obtained by theratio of the average value of the detection data of the sensor module tothe average value of the detection data of the low-frequency calibrationmodule.

In addition to the light attenuation effect of laser sensors, othertypes of sensors may also have a tendency of unstable performance orincreased data errors under long-term high-load working conditions. Byintroducing a low-frequency calibration module, it can be used as arelatively reliable reference to determine whether there is a data shiftphenomenon in the sensor module.

At the same time, since the data of the low-frequency calibration moduleis generally more reliable, when determining which sub-sensor in thesensor module is suspected to be abnormal or abnormal, a more reliablejudgment can be made by increasing the data weight of the low-frequencycalibration module. A simple solution is that all the data from thelow-frequency calibration module is involved in the judgment ofsuspected anomalies with twice the weight.

The low-frequency calibration module can also participate in thejudgment of suspected anomalies by adopting the following schemes todistinguish situations:

1) Single low frequency sensor: the data weight of the low frequencysensor is 2; the data weight of each sub-sensor in the sensor module is1;

2) Two low-frequency sensors: Based on the average value of the sensormodule, the data weight of one low-frequency sensor closer to thereference value is 2, and the weight of the other low-frequencycalibration module is 1.

3) Three or more low-frequency sensors: Based on the average value ofthe data in the low-frequency calibration module, the ones that deviatefarthest from the reference in the sensor module are suspectedanomalies.

When the control module detects a suspected abnormality in one of thesub-sensors in the sensor module, and the suspected abnormal sub-sensorfurther shows sub-sensor abnormality, the sub-sensor is determined as anabnormal sensor, it is isolated and classified into an isolation area,and the multi-core sensor module continues to work normally after it isdegraded.

The disclosure also discloses a method for isolation and restoration fora multi-core sensor system within a taxi. The method is shown in FIG.11. The sensor module obtains a set of detection data at a time, and thecontrol module filters out suspected abnormal data from this set ofdata, and then determines whether the corresponding sub-sensor meets theisolation condition. The sub-sensor was judged to be abnormal sub-sensorand then classified into the isolation zone; after judging that thesuspected abnormal sub-sensor does not meet the isolation condition, thesub-sensor continues to work normally. Determine whether the abnormalsub-sensor entering the isolation area can heal itself. If it can healitself, the frequency reduction will be performed. However, the outputdata of the abnormal sub-sensor will not participate in the calculationof the output data of the main control module. For abnormal sub-sensorsthat cannot self-heal, stop working and notify the operator to repair orreplace them. For the abnormal sub-sensor after frequency reduction, thecontrol module detects its output data to judge whether it meets therecovery condition. If the abnormal sub-sensor can meet recoverycondition, the abnormal sub-sensor that meets the recovery condition isremoved from the isolation zone, and the abnormal sub-sensor will bedetermined as the initial sub-sensor and resume to work. The output datais involved in the calculation of sensor module data or master controldata. For the abnormal sub-sensor that does not meet the recoverycondition, whether it can be self-healing is determined again.

After isolating the abnormal sub-sensors in the sensor module, theaverage value of the remaining sub-sensor output data is used as theoutput result of the sensor module, and the sensor module can continueto be used normally.

The present disclosure sets a rotational rest mode for the sensormodule. Among the sub-sensors that work normally, one or more rotationalrests are selected, which can solve the problem of reduced performancedue to sub-sensor fatigue.

With the increase of working time, the internal state of the sub-sensorwill change to a certain extent. For example, the internal temperaturewill increase with the increase of working time, and the mechanicalcomponents of the sampling device will suffer from metal fatigue.Therefore, an appropriate rest after working for a period of time willrestore the sub-sensor to its optimal working state.

The sub-sensor enters the stable working period after starting for aperiod of time, but after a long period of continuous work, the fatiguewill increase. In order to alleviate this situation, those sub-sensorsthat have entered a fatigue state can be selected to be put into a reststate to reduce the data offset of the sensor fatigue stage, and try tomake the sub-sensor work in a stable working period.

For the laser sensor module, the rotational rest can also keep the lightattenuation of the same group of sub-sensors basically synchronized.

With the use of semiconductor lasers for a long time, there will be aproblem of attenuation of the light output power due to the decrease inthe efficiency of semiconductor materials. When a semiconductor laser isused as a light emitting element, the light scattering emission particlesensor needs to consider the light attenuation synchronization betweensub-sensors when it contains multiple sub-sensors.

If the light attenuation between the sub-sensors is not synchronized,when the light attenuation is light, its impact on the data isrelatively small, although there will be some differences in the data ofeach sub-sensor, but it is impossible to determine whether thesub-sensor is faulty based on these lighter differences. But these datawill still participate in the calculation of the final detection data ofthe sub-sensor and result in deviations in the final detection data.

Therefore, the control module of the multi-core sensor system shouldrecord and store the cumulative working time of each sub-sensor, adjustthe rotational rest interval of each sub-sensor according to thecumulative working time, and keep the light attenuation of eachsub-sensor basically synchronized, which is conducive to the improvementof sub-sensor detection data accuracy.

The disclosure has low use cost. Compared with expensive precisioninstruments, the sensor module only adds a few sub-sensors, which doesnot significantly increase the overall cost of the device. However, Dueto the increase in reliability and accuracy, it is also possible toapply low-precision, low-reliability, low-cost sub-sensors to situationswhere only high-precision instruments can be used. The multi-core sensormodule also extends the life and maintenance cycle of the entiremonitoring equipment, reducing the cost of equipment replacement andrepair.

Sub-sensor failure judgment can be done through the local master controlmodule, or through the data center online monitoring system. The onlinemonitoring system is responsible for receiving data, storing data, dataprocessing, and generating visual pollution cloud maps.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is the schematic diagram of a state of a sub-sensor;

FIG. 2 is the schematic diagram of a single sensor failure of asingle-core sensor module, a dual-core sensor module, a triple-coresensor module, and a triple-core sensor module;

FIG. 3 is the schematic diagram of judging a suspected abnormalsub-sensor module. For one-core and dual-core sensor modules, abnormalconditions cannot be determined after suspected abnormalities; sensormodules with three or more cores can determine sensors suspected ofabnormalities.

FIG. 4 is the sensor error diagram, Do and Di are fluctuations; Do andactual values are drifting;

FIG. 5 is a schematic diagram of the output data of the quad-core sensormodule and its sub-sensor output, Average is the quad-core averageoutput result, and the dotted line is the output result of each core;

FIG. 6 is the schematic diagram of an isolation method for anabnormality of a sub-sensor of a six-core sensor module;

FIG. 7 is a schematic diagram of isolation and recovery an abnormalsensor in a quad-core sensor module;

FIG. 8 is a schematic diagram of the relationship between the detectionaccuracy of the sensor and the temperature;

FIG. 9 shows the relationship between the detection accuracy of thesub-sensor and the measured gas flow rate;

FIG. 10 is a schematic diagram showing the relationship between fanspeed, wind resistance and measured gas flow rate;

FIG. 11 is a flowchart of a method for isolation and recovery amulti-core sensor system;

FIG. 12 is a schematic diagram of the six-core sensor module;

FIG. 13 is a schematic diagram of the quad-core sensor module and itsfault indicator;

FIG. 14 depicts the process of the isolation and recovery method of amulti-core sensor system.

In the drawings: 100—normal sensor, 101—suspected abnormal sub-sensor(one), 102—suspected abnormal sub-sensor (two), 104—abnormal sub-sensor,U3—sensor 3, U3—d-status indicator (Red-fault), U4—d-status indicator(green-normal); 2U (3U)—represents a group of three-core sensorsoperating in two-core mode, with one core isolated.

DETAILED DESCRIPTION OF EMBODIMENTS

The multi-core sensor system includes a gas separation box, a controlmodule and a detection module. The gas separation box is used todistribute the measured gas to each individual sub-sensor. The gas inletof the gas separation box is connected to the gas sampling head, and thegas outlet is connected to the air inlet of each sub-sensor of thedetection module. The detection module is a sensor module with four ormore sub-sensors built in and it is used to detect the concentration ofatmospheric pollutants. The control module is used to receive, analyzeand upload the data detected by the detection module, and supply powerto the detection module. The gas separation box has a buffer function torelieve pressure fluctuations.

The detection module may also include a low-frequency calibration moduleconsisting of at least one sub-sensor, and the sub-sensor in thelow-frequency calibration module is similar to the sub-sensor in thesensor module; the operating frequency of the sub-sensor in thelow-frequency calibration module is much lower than the sub-sensor inthe sensor module. In a multi-core sensor system including alow-frequency calibration module can reduce to two or three sub-sensors.

The control module is provided with a control module data communicationinterface which is connected with the sub-sensor data communicationinterface by wires. The sub-sensor transmits data to the control modulethrough the data communication interface of the control module connectedto the sensor. The detection module is connected to the control modulethrough a data interface. The control module can not only process thedetection data of the sub-sensors, but also upload the data to the datacenter through the wireless network to implement the data uploading andpositioning functions. The data center is responsible for receivingdata, storing data and processing data. The online monitoring system canmanually control the secondary calibration of the abnormal sensor.

The disclosure adopts a sensor module consisting of multiplesub-sensors, which realizes complementary data deviations and mutualverification, and improves the reliability, consistency, accuracy andlife of the sensor module. As shown in FIGS. 3 and 4, 4U represents aquad-core sensor module. When a sub-sensor is detected to have asuspected abnormality, and the suspected abnormal sub-sensor furthershows sub-sensor abnormality, the sub-sensor is determined as anabnormal sensor and isolated. The quad-core sensor module downgraded toa three-core sensor module, the three-core sensor module can still worknormally. 5U means a five-core sensor module. When a sub-sensor isdetected to have a suspected abnormality, and the suspected abnormalsub-sensor further shows sub-sensor abnormality, the sub-sensor isdetermined as an abnormal sensor and isolated, the five-core sensormodule is downgraded to a quad-core sensor module, and the quad-coresensor module can still work normally; and so on, the six-core sensormodule, the seven-core sensor module and more core sensor modules.

Humidity and Temperature Adjustment

The detection accuracy of the sub-sensor is related to temperature. Asshown in FIG. 8, the sensor has an optimal operating temperature range.When the temperature is higher than the optimal operating temperature,the detection accuracy will decrease. In the disclosure, the temperatureof the sensor and the intake air are adjusted by a temperature controldevice.

Embodiment One

The gas separation box can be equipped with a semiconductorrefrigerating sheet capable of heating and dehumidifying. Thesemiconductor refrigeration sheet is metal, and the semiconductorrefrigeration sheet includes a hot end and a cold end. Use the hot endof the semiconductor refrigeration sheet to directly heat the gasseparation box, and install a humidity sensor before the gas inlet ofthe gas separation box. The control module turns on the semiconductorrefrigeration sheet when the humidity of the gas measured is greaterthan the set value upper limit (the upper limit of the set value can be60%, 65%, 70%, etc.); when the gas humidity measured by the humiditysensor is less than the lower limit of the set value (the lower limit ofthe set value can be 40%, 50%, etc.), the control module makes thesemiconductor refrigeration sheet stop heating and dehumidifying.

Embodiment Two

The gas separation box can be equipped with a semiconductorrefrigerating sheet with heating and dehumidification functions. Thesemiconductor refrigeration sheet is metal, and the semiconductorrefrigerating sheet includes a hot end and a cold end. The gasseparation box is directly heated by the hot end of the semiconductorrefrigerating sheet, and the cold end of the semiconductor refrigeratingsheet is connected to the heat-dissipating grille, and theair-distributing box is cooled through the heat-dissipating grille.Install a humidity sensor before the air inlet of the gas separationbox. The control module turns on the semiconductor refrigerating sheetwhen the humidity of the gas measured is greater than the set valueupper limit (the upper limit of the set value can be 60%, 65%, 70%,etc.); when the gas humidity measured by the humidity sensor is lessthan the lower limit of the set value (the lower limit of the set valuecan be 40%, 50%, etc.), the control module makes the semiconductorrefrigeration sheet stop heating and dehumidifying.

Embodiment Three

The gas separation box can be equipped with a semiconductorrefrigerating sheet capable of heating and dehumidifying. Thesemiconductor refrigeration sheet is metal, and the semiconductorrefrigeration sheet includes a hot end and a cold end. Use the hot endof the semiconductor refrigeration sheet to directly heat the gasseparation box. The cold end of the semiconductor refrigerating sheet isconnected to an air pump, and the air-distributing box is cooled throughthe air pump. Install a humidity sensor before the gas inlet of the gasseparation box. The control module turns on the semiconductorrefrigeration sheet when the humidity of the gas measured is greaterthan the set value upper limit (the upper limit of the set value can be60%, 65%, 70%, etc.); when the gas humidity measured by the humiditysensor is less than the lower limit of the set value (the lower limit ofthe set value can be 40%, 50%, etc.), the control module makes thesemiconductor refrigeration sheet stop heating and dehumidifying.

Compensation of Flow Rate, Temperature, Power and Pipeline Length

The detection accuracy of the sub-sensor is also related to the flowrate of the measured gas flowing inside the sensor. As shown in FIG. 9,the detection accuracy of the measured gas is within the range of V₁ toV₂ with the optimal flow rate V₀ as the center. The detection accuracywill be affected if the measured gas flow rate is too fast or too slow.The internal air resistance of the sensor or other reasons will causethe measured gas flow rate to change. As shown in FIG. 10, the presentdisclosure controls the measured gas flow rate to the optimal flow rateby adjusting the internal fan speed (S₁, S₂) or other flow rateadjustment methods. Within range, improve the detection accuracy of thesub-sensor. Multi-core sensor modules use embedded algorithms tocompensate for the problem of asynchronous sampling of multiplesub-sensors caused by different lengths of intake pipes, therebyobtaining more accurate detection data. Similarly, temperature andhumidity are compensated by corresponding algorithms to improve dataaccuracy.

Embodiment Four

By controlling the speed of the fan, the sampling flow is compensated.The flow rate and differential pressure sensor are used to obtain thegas flow rate, and a fan speed control circuit is added at the sametime. The fan speed is controlled by the obtained gas flow rateinformation, so that the sampling gas flow rate is stabilized, within arange of preferred flow rate, as shown in V₀ of FIGS. 9 and 10. Theoptimal flow rate of the sensor is based on experimental methods toobtain empirical values.

Embodiment Five

For the laser particle sensor, multi-core sensors comprise a laser powerdetection device and a laser power control circuit which are configuredto compensate the laser power. The change relationship of the particleconcentration value corresponding to each laser power value is obtainedexperimentally (that is, other conditions are fixed and only themeasured conditions are changed to obtain the measurement results). Theattenuation data is compensated by the laser power control circuitaccording to the detection result of the laser power detection device.

Embodiment Six

Take temperature compensation measures for the sensor. Install atemperature acquisition probe on the sensor or the measured gas. First,by using the experimental method or the sensor temperaturecharacteristic data, the change relationship of the pollutantconcentration value corresponding to different sampling temperaturevalues is obtained (that is, other conditions are fixed and only themeasured temperature conditions are changed). Compensate the outputpollutant results based on the collected temperature data during use.

Embodiment Seven

Take humidity compensation measures for the sensor.

Install humidity acquisition equipment to collect the humidity data ofthe measured gas.

First, by using the experimental method or the humidity characteristicdata of the sensor, the change relationship of pollutant concentrationvalues corresponding to different sampling humidity values is obtained(that is, other conditions are fixed and only the humidity conditions ofthe measured gas are changed).

Compensate the output pollutant results based on the collected humiditydata during use.

Output Data Calculation Method

Multi-core sensor modules use multiple sub-sensors to measure airquality at the same time, and the output value is the result ofcomprehensive calculation of data from multiple sensors. The data issmoother, more stable, and more accurate.

The eighth embodiment to the twelfth embodiment are data calculationmethods of the sensor module. The data of the outlier sensor needs to beexcluded during data calculation. For the method of determining theoutlier sensor, refer to the thirteenth to the seventeenth embodiments.

In the case of the sensor module and the low-frequency calibrationmodule, when the low-frequency calibration module generates data, itsdata can be used as more reliable detection data to participate in thecalculation of the output data of the sensor module.

Considering that the data of the low-frequency calibration module ismore reliable, the data of the low-frequency calibration module can begiven a double weight to be added to the calculation.

Embodiment Eight

Mean value method: a method for calculating the output data of a sensormodule; after excluding abnormal sub-sensor data, the average value ofall normal sub-sensor data is taken as the output result.

Embodiment Nine

Median method: a method for calculating the output data of a sensormodule; after excluding abnormal sub-sensor data, the values of allnormal sub-sensor are sorted, and the middle value of the sort is usedas the final result.

The number is even, and then the average of the two sub-sensors in themiddle is taken as the final result.

Embodiment Ten

Correlation coefficient method: a method for calculating the output dataof a sensor module; after excluding abnormal sub-sensor data, calculatethe normal sub-sensor data as follows to obtain the final result.

The storage unit stores the historical detection data of eachsub-sensor, and calculates the values of the judged sub-sensor and othersub-sensors by using the historical data of a period (1 minute, 10minutes, 20 minutes, . . . 1 hour) as a time unit.

Correlation coefficient, the calculation method of the above correlationcoefficient:

A. Obtain the value of the historical time unit of the judged sub-sensorand the average value of other sub-sensors in that period to calculatethe correlation coefficient.

B. Obtain the value of the historical time unit of the judged sub-sensorto calculate the correlation coefficient with each of the othersub-sensors. After obtaining the result, calculate the average value ofeach correlation coefficient as the final correlation coefficient toobtain each normal sub-sensor. After correlating coefficients with othersub-sensors, calculate the percentages of the correlation coefficientsof all normal sub-sensors to the sum of the total correlationcoefficients. Multiply the detection result of each normal sub-sensor bythis percentage and add up to get the final detection result.

Embodiment Eleven

Variance method: a method for calculating the output data of the sensormodule; after excluding the abnormal sub-sensor data, the normalsub-sensor data is calculated as follows to obtain the final result.

The memory stores the historical detection data of each sub-sensor, anduses the historical data of a period of time (1 minute, 10 minutes, 20minutes, . . . 1 hour) as the time unit to calculate the variance Vi (orstandard deviation), add the variance of each sub-sensor and calculatethe difference between the sum and the variance of each sub-sensor.After obtaining the difference, calculate the percentage of the sum ofthe difference of each sub-sensor. The detection result of thesub-sensor is multiplied by the percentage and then added up to obtainthe final detection result.

Embodiment Twelve

Percentage method: A method for calculating output data of a sensormodule. After excluding abnormal sub-sensor data, the normal sub-sensordata is calculated as follows to obtain the final result.

The sensor stores the historical detection data of each sub-sensor, anduses a period of time (10 seconds, 20 seconds, etc.) as the time unit tocalculate the average value of the detection value in the nearest timeunit, and uses the average value to calculate. The above calculationmethod:

A. Add up the average value of each sub-sensor in the time unit tocalculate the percentage of each sub-sensor in the sum, and multiply thedetection result of each normal sub-sensor by the percentage to add thefinal result.

B. Using the calculation method described above, calculate thepercentage of each sub-sensor in multiple time units closest to thecurrent, average the percentage of each sub-sensor in multiple timeunits, and get the average of each sub-sensor in multiple time unitsclosest to the current, the detection result of each normal sub-sensoris multiplied by the percentage and then added up to obtain the finaldetection result.

Identify Sub-Sensor Working Status

This solution provides a method for isolation and restoration for amulti-core sensor system within a taxi. This method is shown in FIG. 11.

1) Judgment of the abnormal sub-sensor: The sensor module obtains a setof detection data at a time, and the control module filters outsuspected abnormal data from this set of data, and then determineswhether the corresponding sub-sensor meets the isolation condition.

2) Isolation of an abnormal sub-sensor: The sub-sensor was judged to beabnormal sub-sensor and then classified into the isolation zone; thesensor module continues to work after it is degraded. The abnormalsub-sensor entering the isolation zone can stop working or continuesampling and detection, but the data output by the abnormal sub-sensordoes not participate in the calculation of the output data of thecontrol module.

3) Determine whether the abnormal sub-sensor entering the isolation zonecan heal itself: if it is judged that it can heal itself, then performfrequency reduction work on the self-healing abnormal sub-sensor, and ifthe abnormal sub-sensor cannot heal itself, the operation andmaintenance party is notified for the repair or replacement.

4) Recovery of abnormal sub-sensors: Monitor the output data of theabnormal sub-sensors entering the isolation zone to determine whetherthey have reached the recovery conditions. If the recovery conditionsare met, the sub-sensors meeting the recovery conditions are detachedfrom the isolation zone, and the abnormal sub-sensor is determined asthe initial sub-sensor and resume to work.

Embodiment Thirteen

Judgment of suspected abnormal sub-sensor and abnormal sub-sensor: Whenthe variance of the data of a certain sub-sensor exceeds the threshold,or the drift of the data of the sub-sensor exceeds the threshold, firstlist it as a suspected abnormal sensor instead of immediatelyidentifying the sensor as abnormal. Finally, it is determined that thesub-sensor is abnormal only when multiple consecutive data are abnormalin a certain period of time.

Embodiment Fourteen

Comparison method of average values of sub-sensors: Take a quad-coresensor module as an example, and use the current time as a reference tocompare the data of one sub-sensor with the average value of other threesub-sensors within a certain period of time (such as 5 s average, 30 saverage, 60 s average, etc.)

Embodiment Fifteen

When the abnormal sub-sensor occurs, the data collected by it should beisolated and not involved in the calculation of the final output data ofthe sensor module. However, the abnormal sub-sensor still normallyoutputs data to the control module to monitor the data of the abnormalsub-sensor. Abnormal sub-sensor includes abnormal drift of sub-sensor,abnormal fluctuation of sub-sensor and abnormal correlation ofsub-sensor.

The storage unit stores the historical detection data of eachsub-sensor, and calculates respectively the value correlationcoefficient of the target sub-sensor and other sub-sensors by using thehistorical data of a period (1 minute, 10 minutes, 20 minutes, . . . 1hour) as a time unit. If the correlation coefficient is less than acertain value, such as 0.5 (non-strong correlation), the correlation ofthe sensor is judged to be abnormal, and it does not participate in thecalculation of the final result. The specific process of calculating thecorrelation coefficient is as follows:

A. Obtain the value of the historical time unit of the target sub-sensorand the average value of other sub-sensors in that period to calculatethe correlation coefficient.

B. Obtain the value of the historical time unit of the sub-sensor tocalculate respectively the correlation coefficient with each of othersub-sensors, and calculate the average value of each correlationcoefficient as the final correlation coefficient after obtaining theresult.

The correlation method is used to determine the abnormal correlation ofsub-sensor. Taking the correlation calculation of a quad-core sensormodule as an example, the correlation between the 100 sets of data ofthe sub-sensors and the average of the 100 sets of data of the otherthree sub-sensors is used for correlation calculation. If the R₂ is lessthan or equal to 0.8, it indicates that the correlation of thesub-sensors is abnormal, and the sub-sensor data is isolated. The sensormodule selects the data of the other three sub-sensors to calculate andoutput the monitoring results.

Embodiment Sixteen

The sixteenth embodiment is a method for determining the abnormalfluctuation of sub-sensor. The storage unit stores the historicaldetection data of each sub-sensor, and uses the historical data of aperiod of time (1 minute, 10 minutes, 20 minutes, . . . 1 hour) as thetime unit to calculate the variance (or standard deviation), bycomparing the variance (or standard deviation) of the target sub-sensorwith the variance (or standard deviation) of other sub-sensors, thespecific process of above variance comparison method is as follows:

A. Compare the variance (or standard deviation) of the target sub-sensorwith the mean value of the variance (or standard deviation) of othersub-sensors. If the difference between the two exceeds a certain value,such as 20%, 30%, etc., the abnormal fluctuation of the sub-sensor willbe judged.

B. Compare the variance (or standard deviation) of the target sub-sensorwith the variance (or standard deviation) of other sub-sensorsrespectively, and calculate the percentage of the difference between thetwo relative to the variance (or standard deviation) of the comparedsub-sensor. Select the maximum value of percentage. If it exceeds acertain value, such as 20%, 30%, etc., it is judged that the abnormalfluctuation sub-sensor.

Embodiment Seventeen

The seventeenth embodiment is a method for judging the abnormal drift ofsub-sensor. The difference between the average value of the targetsensor in the past two time units is calculated, and the percentage ofthe difference value and the average value in the latest time unit iscalculated, and the percentage is used for judgment. The specificprocess of above drift judgment method is as follows:

A. Compare the percentage obtained by the target sub-sensor with theaverage of the percentages obtained by other sub-sensors. If thepercentage difference exceeds a certain value, such as 20%, 30%, 40%,etc., the sub-sensor is judged to be drifting abnormally.

B. Compare the percentage obtained by the target sub-sensor with theaverage of the maximum value obtained by other sub-sensors. If thepercentage difference exceeds a certain value, such as 20%, 30%, 40%,etc., the sub-sensor is judged to be drifting abnormally.

Embodiment Eighteen

In the case of the need to isolate the abnormal sensor, the data of theabnormal sensor is isolated, but the fan or air pump of the abnormalsensor continues to keep running, to ensure that the wind pressure andflow are constant, and to reduce pressure fluctuations.

Embodiment Nineteen

As shown in FIG. 13, install the status indicator light on thesub-sensor. After the abnormal sub-sensor U3-3 is identified, the statusindicator light U3-d at the corresponding position on the communicationport of the circuit board will change to a warning color (such as red).The status indicator light U4-d corresponding to the sub-sensor innormal working state is green.

Rotational Rest Mode

The disclosure sets a rotational rest working mode for the sensormodule. Among the sub-sensors that work normally, one or more rotationrests are selected, that is, the fatigue problem of the sub-sensor issolved by actively degrading the operation. For the laser sensor module,the rotational rest can also keep the light attenuation of the samegroup of sensors basically synchronized.

Common single-rotational rest conditions include:

1) The sub-sensor with the longest time to enter the fatigue state;

2) The sub-sensor closest to entering the fatigue state;

3) The sub-sensor with the longest accumulated working time;

4) The sub-sensor with the least accumulated rotational rest;

5) When the temperature data of the sub-sensor can be obtained, thesub-sensor with the highest temperature;

6) Suspected abnormal sensor.

The sub-sensors selected by using different rotational rest conditionsmay be inconsistent. In actual application, multiple rotational restconditions may be given weights or priorities to quantitativelydetermine which sub-sensor is allowed to enter the rotational rest.

Considering that the fatigue problem is a periodic recurrence problem,ideally, each sub-sensor should get a rest cycle before it enters thefatigue state. Assume that the average stable working time of thesub-sensor is T. For the module of N sensors, if the strategy ofsuccessive rotational rest of each sub-sensor in the sensor module isadopted, the interval between the two consecutive rotational restsshould not be longer than T/N to ensure that each sensor can enter therotational rest in time.

If T=8 hours, the sensor module consisting of 4 sub-sensors can berotated every 2 hours using the sequential rotational rest strategy,which can ensure that each sub-sensor can enter the rotational restbefore entering the fatigue state.

A status indicator is installed on the sub-sensor. When an abnormalsub-sensor is identified, the color of the status indicator of the lightcorresponding sub-sensor changes to a warning color; the statusindicator light corresponding to the sub-sensor in normal working statusis continuous green. The status indicator light corresponding to thesub-sensor that enters the rotational rest state is green that turns onand off alternately.

Embodiment Twenty

The twentieth embodiment is a rotational rest mode of a sub-sensor. Forsensor modules, rotational rest refers to turning off the sensing partof one or more sub-sensors within a specified time. For example, thelaser particle sensor module using a fan only turns off the laser, andthe fan does not turn off.

The off time of the sub-sensor can be a fixed time (such as 1 hour, 2hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10hours, 11 hours, 12 hours, 24 hours, 2 days, 3 days, 4 days, 5 days, 6days, or 7 days, etc.), after the closed sub-sensor reaches the closingtime, the closed sub-sensor is activated, and then the next sub-sensorthat meets the rotational rest condition is closed. The closed time canalso be determined according to the working status of other sub-sensors.For example, in a quad-core sensor module with one sub-sensor in the offstate. At this time, if the system determines that one of the threesub-sensors in operation has reached the isolation condition and needsto be isolated, the sub-sensor in the closed state should be immediatelyenabled. The specific rotation conditions can be:

A. Select the rotational rest sensor based on the temperature change.Form 1: Select the sub-sensor with the highest temperature through theacquired sub-sensor temperature data; Form 2: Select the sub-sensor thatis turned off according to the ambient temperature. If the ambienttemperature is higher than the temperature set value (such as 40 degreesCelsius), it will be numbered turn off sub-sensors in turn;

B. Select the rotational rest sensor by detecting the change in thevalue. For confirmed suspected abnormal sensor shut down preferentially.

Embodiment Twenty-One

When three-core or more sub-sensors in the quad-core sensor module worknormally, a single-core rotational rest scheme can be adopted. Theworking state of the sub-sensor is greatly affected by temperature. Whenthe temperature is higher than 60° C. or after four hours of normaloperation, the adjacent single-core cycle rest is changed, and the restis rotated in order to reduce the working time of the sub-sensor underhigh temperature and increase the working time limit of the quad-coresensor.

What is claimed is:
 1. A method for isolation and restoration for amulti-core sensor system within a taxi, the taxi comprising a car body,a roof light and a multi-core sensor system; the roof light beinginstalled on the car body, and the multi-core sensor system beinginstalled in the roof light; the multi-core sensor system comprising acontrol module and a detection module; the detection module comprising asensor module consisting of at least two sub-sensors of the same type;the detection module further comprising a low-frequency calibrationmodule consisting of at least one sub-sensor that is of the same type asthe sub-sensors of the sensor module, wherein the at least onesub-sensor in the low-frequency calibration module operates at a sensingfrequency lower than that of the sub-sensors in the sensor module; themethod comprising the following steps: 1) judgment of an abnormalsub-sensor: the control module receives a set of detection data obtainedby the sensor module, and receives the detection data obtained by thelow-frequency calibration module; and the control module filters outsuspected abnormal data from the set of detection data obtained by thesensor module together with the detection data obtained by thelow-frequency calibration module, by a way of applying a double weighton the detection data obtained by the low-frequency calibration modulein filtering out suspected abnormal data, and then determines whetherthe sub-sensor corresponding to the suspected abnormal data meets anisolation condition; 2) isolation of the abnormal sub-sensor: asub-sensor meeting the isolation condition is determined as an abnormalsub-sensor, and is classified into an isolation zone; and the sensormodule is degraded and continues to work; 3) determining whether thesub-sensor entering the isolation zone can heal itself; if it is judgedthat it can heal itself, then lowering the sensing frequency of theself-healing abnormal sub-sensor, wherein data output by the abnormalsub-sensor does not participate in calculation of the output data of thecontrol module; and notifying an operation and maintenance party forrepair or replace for the abnormal sub-sensor which cannot heal itself;and 4) recovery of the abnormal sub-sensor: monitoring the output dataof the abnormal sub-sensor entering the isolation zone to determinewhether the abnormal sub-sensor has reached recovery conditions; if therecovery conditions are met, the sub-sensor that meets the recoveryconditions is detached from the isolation zone, and the abnormalsub-sensor resumes to work.
 2. The method of claim 1, wherein a ratio ofsensing frequencies between the sub-sensor of the sensor module and thesub-sensor of the low-frequency calibration module is 2:1, 3:1, 4:1,5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 15:1, or 20:1.
 3. The method of claim 1,wherein the abnormal sub-sensor is judged according to one of thefollowing abnormalities: 1) abnormal drift of sub-sensor; 2) abnormalfluctuation of sub-sensor; and 3) abnormal correlation of sub-sensor. 4.The method of claim 1, wherein a status indicator light is on thesub-sensor; when the abnormal sub-sensor is identified, the statusindicator light on the abnormal sub-sensor is changed to a warningcolor; otherwise, the status indicator light in green.
 5. The method ofclaim 1, wherein the detection module is configured to detect aconcentration of atmospheric pollutants; and the control module isconfigured to receive, analyze and upload the data detected by thedetection module.
 6. The method of claim 5, wherein after receiving aset of detection data from the detection module, the control moduleanalyses the set of detection data and calculates an output dataaccording to one of the following methods: 1) mean value method; 2)median method; 3) correlation coefficient method; 4) variance method; 5)percentage method, wherein the data of the abnormal sub-sensor isfiltered out.
 7. The method of claim 5, wherein the accuracy of themulti-core sensor system is improved by one of the following methods: 1)compensation for lengths difference: embedded algorithms are used tocompensate for asynchronous sampling of the sub-sensor caused bydifferent lengths of intake pipes; 2) temperature compensation: atemperature acquisition probe is installed at the sensor or the measuredatmospheric gas; by using the experimental method or temperaturecharacteristic data of the sub-sensor, a change relationship of thepollutant concentrations corresponding to different samplingtemperatures is obtained; output results of the multi-core sensor systemare adjusted according to measured atmospheric gas temperature; and 3)humidity compensation: a humidity acquisition device is installed tomeasure gas humidity; by using the experimental method or humiditycharacteristic data of the sub-sensor, a change relationship of thepollutant concentrations corresponding to different sampling humidityvalues is obtained; output results of the multi-core sensor system areadjusted according to measured gas humidity.
 8. The method of claim 5,wherein the sub-sensor is one of the following sensors: PM₁ sensor,PM_(2.5) sensor, PM₁₀ sensor, PM₁₀₀ sensor, sulphur dioxide sensor,nitrogen oxide sensor, ozone sensor, carbon monoxide sensor, VOCssensor, and TVOC sensor.
 9. The method of claim 5, wherein thesub-sensor is a laser particle sensor; the multi-core sensor systemcomprise a laser power detection device and a laser power controlcircuit; the multi-core sensor system improves the accuracy of detectiondata of the sensor module by compensation for laser power whichcomprises the following steps: a change relationship of the particleconcentration value corresponding to each laser power value is obtainedexperimentally; and the attenuation data is compensated by the laserpower control circuit according to the detection result of the laserpower detection device.